Course Tables Overview
Most courses listed in the tables below are offered on a yearly basis, although some may be offered every other year. The semesters listed in the tables below indicate the likely semester(s) in which each course is next likely to be offered, as indicated by the participating academic units. Similarly, the instructor listed is the likely instructor. The information in the tables is provided for planning programs of study. However, whether a course is offered in any particular year and by which instructor needs to be confirmed for that year and semester needs to be confirmed by consulting the official course schedules in MaineStreet through the UMaine Portal (or the publicly accessible UMaineOnline Course Search). Latest graduate course descriptions may be accessed through the UMaine graduate catalog.
Only three external courses from other University campuses may typically be included in a graduate student’s program of study. Maine Business School (MBS) graduate courses are viewed as internal courses regardless of which campus they originate.
Note: Courses designated with an asterisk (*) are typically available both online and in-person. Unless otherwise specified, courses are 3 credits.
Foundation Courses
Admitted candidates missing appropriate background prerequisite courses will take one foundation course in each of the three foundation areas of statistics, programming, and systems as appropriate and as advised by their graduate committee and/or advisor. Foundation courses may count towards the degree if approved on the student’s Graduate Program of Study. Currently approved courses within the three foundation areas include:
All students are expected to take all three foundation courses unless the courses would be repetitive of past coursework to the extent that waivers should be granted.
Required Courses
Whether in a graduate degree or a graduate certificate program, all students must complete the following introductory course. This interdisciplinary team-taught course is structured around an overview of data science and engineering topics and tools as applied to large case study data sets.
- DSE 510: Practicum in Data Science and Engineering *
- Usual Semester: Spring, Prereq: program admission and SIE 507, or instructor permission
The following courses are all required for all students pursuing the MS Data Science and Engineering with Thesis Option.
- SIE 501: Introduction to Graduate Research – (1 Credit) *
- Usual Semester: Spring, Prereq: program admission
- SIE 502: Research Methods – (1 Credit) *
- Usual Semester: Fall, Prereq: SIE 501
- INT 601: Responsible Conduct of Research – (1 Credit) *
- Usual Semester: Fall/Spring, Prereq: graduate standing
Theme Areas
Domain Specialisation Courses
A single course may not count under more than one domain specialization or theme category. Courses currently contained within the domain specializations include the following:
Note: Courses designated with an asterisk (*) are typically available both online and in-person. Unless otherwise specified, courses are 3 credits.
Artificial Intelligence (AI) Coursework Focus Areas
The DSE program does not have officially designated concentrations, specializations or graduate certificates in Artificial Intelligence currently. However, several in depth courses in AI may be pursued as part of and count towards the MS Data Science and Engineering degree. The primary factor controlling whether a student may take any or several of these courses is dependent upon the student’s previous coursework background. Students with undergraduate degrees in computer science, engineering, and math are more likely to have fewer or no prerequisites to make up in order to take the following courses.
AI Applications in Business
Prerequisites: A recommended overall prerequisite for all of the BUA courses is BUA 601 Data Analysis for Business as well as the DSE Foundation Courses or equivalents.
Although not explicitly focused on AI concepts or applications, any of the previously listed business analytics courses in the DSE program (i.e., BUA designators) may provide the analytic foundations for better understanding current and future uses of AI in the business environment. In particular, BUA 680 covers general philosophical principles underlying all business analytics activities and BUA 685 covers empirical causal modeling with Bayesian belief networks for business applications, which is one of the most promising areas in applied AI in the rapidly emerging big data era.
Artificial Intelligence (AI) Courses Offered by the School of Computing and Information Science
Prerequisites: It is recommended that a student pursuing these courses should have, at a minimum, prerequisite coursework in object-oriented design, programming, and data structures (e.g., COS 225 and 226 or their equivalents), two semesters of calculus, statistics (at least at the level of STS 232 but preferably at the level of STS 332, 434, or 435), and experience in software development.
Artificial Intelligence (AI) Courses Offered by the Department of Electrical and Computer Engineering
Prerequisites: It is recommended that a student pursuing these courses should have, at a minimum, prerequisite coursework that includes three semesters of calculus, a calculus-based statistics course, and engineering-level programming skills.
